Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Model Configuration & Training
The model configuration is very straightforward: We create both encoder and
decoder models, use them as arguments to the large EncoderDecoder model that
handles the boilerplate, and create a loss and an optimizer as usual.
Model Configuration
1 torch.manual_seed(23)
2 encoder = Encoder(n_features=2, hidden_dim=2)
3 decoder = Decoder(n_features=2, hidden_dim=2)
4 model = EncoderDecoder(encoder, decoder,
5 input_len=2, target_len=2,
6 teacher_forcing_prob=0.5)
7 loss = nn.MSELoss()
8 optimizer = optim.Adam(model.parameters(), lr=0.01)
Next, we use the StepByStep class to train the model:
Model Training
1 sbs_seq = StepByStep(model, loss, optimizer)
2 sbs_seq.set_loaders(train_loader, test_loader)
3 sbs_seq.train(100)
fig = sbs_seq.plot_losses()
Figure 9.7 - Losses—encoder + decoder
Encoder-Decoder Architecture | 703